Instructions to use async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw")
model = AutoModelForCausalLM.from_pretrained("async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw

SGLang

How to use async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw with Docker Model Runner:
```
docker model run hf.co/async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw
```

Configuration Parsing Warning:In config.json: "quantization_config.bits" must be an integer

Rombos-LLM-V2.5-Qwen-0.5b

Rombos-LLM-V2.5-Qwen-0.5b is a continues finetuned version of Qwen2.5-0.5B. I noticed recently that the Qwen team did not learn from my methods of continuous finetuning, the great benefits, and no downsides of it. So I took it upon myself to merge the instruct model with the base model myself using the Ties merge method

This version of the model shows higher performance than the original instruct and base models.

Quants:

GGUF: https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-0.5b-GGUF

Downloads last month: 4

Model tree for async0x42/Rombo-LLM-V2.5-Qwen-0.5b-exl2_4.65bpw

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(253)

this model